I'm a Private AI and data consultant for UK membership bodies, mid-sized charities and research organisations

One straight answer, from systems that were never built to talk to each other

One question. One cited answer.

Your CRM, your accounts, your spreadsheets and your documents each hold part of the picture, and none of them talk to each other. I build private AI assistants that read across all of them at once and give you a single, checkable answer, in seconds instead of a morning of exports. Nothing leaves your control.

You’d ask things like:

“Which members lapsed this year, and which of them are still coming to events?” (answered from CiviCRM and Google Sheets)

“Our spring appeal raised £18,240. The accounts show £17,865. Where’s the difference?” (answered from CiviCRM and Xero)

“What does our safeguarding policy say before I share Katie’s case notes with her school?” (answered from Google Docs and CiviCase)

In this video, the demo reads all four of a sample charity's systems at once to pull one wetland appeal update together: its CRM (CiviCRM), its finance system (Xero), its documents (Google Docs) and its survey log (Google Sheets). Nothing in this demo left the private account it runs in. See the full walkthrough →

This is what I build for UK membership bodies, mid-sized charities and research organisations: a private AI assistant connected to the systems you already run, on data that never leaves your control. Ask it a question and it reads across whichever of your systems the answer needs, one or all of them at once, and catches what no single system can show on its own.

How do you stop them making things up?

You have probably seen AI make things up. There is a word for it, hallucinating: the model states something false as confidently as it states something true. With any AI it is a real risk, and the right first question to ask.

Here is how your private AI assistants are built to avoid it. Four habits.

1. They never do the arithmetic. Every count and total is worked out by ordinary code reading your systems. The AI's job is to phrase the answer, not to work it out.

2. They would rather say "I don't know" than guess. If the answer isn't in your systems, they tell you that, instead of filling the gap with something that sounds right. An answer that can't be checked is held back before it reaches you.

3. Every figure links to its source, and the link is built from the record itself. The assistant never writes the link, so it cannot invent one. Click it and the real record opens.

4. They are tested before they reach your team. Against data where the right answers are already known, again and again, with a single wrong answer counting as a fail.

Here's how I test AI assistants and what the testing catches →

What happens next

You don't start with a build. You start with a short, fixed price discovery: a couple of weeks spent working out what your team is already doing with AI, where your data actually lives, and the one thing worth building first. You get a written report and a call, and no obligation to go further.

I work directly with the people accountable for the decision: chief executives, trustees, heads of data, operations, membership or fundraising, and DPOs. Not procurement, not recruiters, not agencies. See a sample discovery report →

"We have worked with many developers and can confidently say that Pete is by far the best, an exceptional coder and consultant with an impressive skill set."

Kathryn Maxwell, IT Project Manager, Royal Meteorological Society

"He is a joy to work with as he is flexible, efficient and a great communicator. We now have a system which is far more intuitive for our visitors and 7,000+ members, which ultimately allows us to better achieve our mission of supporting the interdisciplinary research community."

Joe Yates & Steph Ray, The London School of Hygiene & Tropical Medicine